In [31]:
train.target.hist()
Out[31]:
<AxesSubplot:>
import pandas as pd
import numpy as np
import math
# models
from sklearn.linear_model import LinearRegression, Ridge, Lasso, BayesianRidge, LogisticRegression
from sklearn.svm import SVC
from sklearn.neural_network import MLPRegressor
from sklearn.ensemble import RandomForestClassifier, AdaBoostRegressor, GradientBoostingRegressor
from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier
from catboost import CatBoostRegressor, CatBoostClassifier
from sklearn.ensemble import VotingRegressor
from xgboost import XGBRegressor, XGBClassifier
# processing
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn.model_selection import train_test_split, cross_val_score,cross_validate
from mlxtend.plotting import plot_confusion_matrix
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.preprocessing import StandardScaler
from imblearn.under_sampling import NearMiss
from sklearn.metrics import roc_auc_score
from sklearn.preprocessing import label_binarize
from sklearn.decomposition import PCA
from sklearn.metrics import mean_squared_error
# plot
import matplotlib.pyplot as plt
import seaborn as sns
# config
def make_report(y_pred , y_true):
print ("")
print ("Classification Report: ")
print (classification_report(y_true, y_pred))
cm = confusion_matrix(y_true, y_pred)
fig, ax = plot_confusion_matrix(conf_mat=cm)
plt.show()
def reg_report(true, pred, name='Test', is_print = True):
acc = ((pred<=true*1.1) & (true*0.9<=pred)).mean()
rmse = mean_squared_error(true, pred, squared=False)
if is_print:
print("\n{} Results :\n".format(name))
print('RMSE :',rmse)
print('Accuracy with 5% :', acc)
return rmse, acc
def eval_report(y_train, pred_train,y_test, pred_test,is_print = True):
train_rmse, train_acc = reg_report(y_train, pred_train,is_print=is_print, name='Train',)
test_rmse, test_acc = reg_report(y_test, pred_test,is_print=is_print, name='Test')
return train_rmse, train_acc, test_rmse, test_acc
pd.set_option('display.max_columns', 50)
path = 'datasets/University/datamining q26/'
train = pd.read_csv(path+'training_data.csv')
test = pd.read_csv(path+'tournament_data.csv')
test = test[test.data_type == 'validation']
print(train.shape)
print(test.shape)
feats = train.columns[3:-2]
train['label'] = train['target']*4
test['label'] = test['target']*4
train['label_2vsA'] = (train['label']==2)*1
test['label_2vsA'] = (test['label']==21)*1
train.head()
(501808, 314) (137779, 314)
| id | era | data_type | feature_intelligence1 | feature_intelligence2 | feature_intelligence3 | feature_intelligence4 | feature_intelligence5 | feature_intelligence6 | feature_intelligence7 | feature_intelligence8 | feature_intelligence9 | feature_intelligence10 | feature_intelligence11 | feature_intelligence12 | feature_charisma1 | feature_charisma2 | feature_charisma3 | feature_charisma4 | feature_charisma5 | feature_charisma6 | feature_charisma7 | feature_charisma8 | feature_charisma9 | feature_charisma10 | ... | feature_wisdom25 | feature_wisdom26 | feature_wisdom27 | feature_wisdom28 | feature_wisdom29 | feature_wisdom30 | feature_wisdom31 | feature_wisdom32 | feature_wisdom33 | feature_wisdom34 | feature_wisdom35 | feature_wisdom36 | feature_wisdom37 | feature_wisdom38 | feature_wisdom39 | feature_wisdom40 | feature_wisdom41 | feature_wisdom42 | feature_wisdom43 | feature_wisdom44 | feature_wisdom45 | feature_wisdom46 | target | label | label_2vsA | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | n000315175b67977 | era1 | train | 0.00 | 0.50 | 0.25 | 0.00 | 0.5 | 0.25 | 0.25 | 0.25 | 0.75 | 0.75 | 0.25 | 0.25 | 1.00 | 0.75 | 0.5 | 1.0 | 0.5 | 0.00 | 0.50 | 0.50 | 0.00 | 0.0 | ... | 0.25 | 0.50 | 1.00 | 0.75 | 0.50 | 0.50 | 1.00 | 0.25 | 0.5 | 0.50 | 0.50 | 0.75 | 1.00 | 1.00 | 1.00 | 0.75 | 0.50 | 0.75 | 0.50 | 1.00 | 0.50 | 0.75 | 0.50 | 2.0 | 1 |
| 1 | n0014af834a96cdd | era1 | train | 0.00 | 0.00 | 0.00 | 0.25 | 0.5 | 0.00 | 0.00 | 0.25 | 0.50 | 0.50 | 0.00 | 0.50 | 0.00 | 0.50 | 0.5 | 0.5 | 0.5 | 0.50 | 0.25 | 0.25 | 0.50 | 0.0 | ... | 1.00 | 0.25 | 0.75 | 1.00 | 0.25 | 0.25 | 1.00 | 0.50 | 0.5 | 0.50 | 0.75 | 0.75 | 0.75 | 1.00 | 1.00 | 0.00 | 0.00 | 0.75 | 0.25 | 0.00 | 0.25 | 1.00 | 0.25 | 1.0 | 0 |
| 2 | n001c93979ac41d4 | era1 | train | 0.25 | 0.50 | 0.25 | 0.25 | 1.0 | 0.75 | 0.75 | 0.25 | 0.00 | 0.25 | 0.50 | 1.00 | 0.50 | 0.75 | 0.5 | 0.5 | 1.0 | 0.50 | 0.50 | 0.50 | 0.25 | 0.0 | ... | 0.00 | 0.50 | 0.75 | 0.00 | 1.00 | 0.50 | 0.50 | 0.75 | 1.0 | 0.75 | 1.00 | 0.25 | 0.50 | 0.25 | 0.50 | 0.00 | 0.00 | 0.50 | 1.00 | 0.00 | 0.25 | 0.75 | 0.25 | 1.0 | 0 |
| 3 | n0034e4143f22a13 | era1 | train | 1.00 | 0.00 | 0.00 | 0.50 | 0.5 | 0.25 | 0.25 | 0.75 | 0.25 | 0.50 | 0.50 | 0.50 | 0.75 | 0.50 | 1.0 | 0.5 | 0.5 | 0.00 | 1.00 | 0.00 | 0.75 | 0.0 | ... | 0.75 | 1.00 | 0.75 | 1.00 | 0.75 | 1.00 | 1.00 | 0.00 | 0.5 | 0.75 | 0.75 | 1.00 | 0.75 | 1.00 | 1.00 | 0.75 | 0.75 | 1.00 | 1.00 | 0.75 | 1.00 | 1.00 | 0.25 | 1.0 | 0 |
| 4 | n00679d1a636062f | era1 | train | 0.25 | 0.25 | 0.25 | 0.25 | 0.0 | 0.25 | 0.50 | 0.25 | 0.25 | 0.50 | 0.25 | 0.25 | 0.75 | 0.50 | 0.0 | 0.5 | 0.5 | 0.25 | 0.00 | 0.50 | 0.00 | 0.5 | ... | 0.50 | 0.75 | 0.75 | 0.50 | 0.75 | 0.25 | 0.75 | 0.50 | 0.5 | 0.25 | 0.25 | 0.75 | 0.50 | 0.75 | 0.75 | 0.25 | 0.50 | 0.75 | 0.00 | 0.50 | 0.25 | 0.75 | 0.75 | 3.0 | 0 |
5 rows × 316 columns
train.target.hist()
<AxesSubplot:>
train.era.hist()
<AxesSubplot:>
cor = train.corr()
display(cor['target'].sort_values()[:-2])
feature_dexterity7 -0.012175
feature_dexterity6 -0.011807
feature_dexterity4 -0.011706
feature_charisma69 -0.010858
feature_dexterity11 -0.010820
...
feature_charisma18 0.010509
feature_charisma19 0.010543
feature_charisma37 0.010719
feature_strength14 0.011353
feature_strength34 0.012310
Name: target, Length: 311, dtype: float64
plt.figure(figsize=(30,30))
sns.heatmap(cor, annot=True)
plt.savefig(path+'corrall.jpg')
plt.show()
def func(x):
for i in range(10):
x = x.replace(str(i), '')
return x
kind = set(map(func,feats.values))
print(kind)
{'feature_dexterity', 'feature_constitution', 'feature_intelligence', 'feature_strength', 'feature_wisdom', 'feature_charisma'}
result = {}
for k in kind:
l = []
for feat in feats:
if k in feat:
l.append(feat)
result[k] = l
new_train = pd.DataFrame(columns = ['feature_dexterity', 'feature_constitution', 'feature_intelligence', 'feature_strength', 'feature_wisdom', 'feature_charisma'])
new_test = pd.DataFrame(columns = ['feature_dexterity', 'feature_constitution', 'feature_intelligence', 'feature_strength', 'feature_wisdom', 'feature_charisma'])
for k in kind:
new_train[k] = train[result[k]].mean(axis = 1)
new_test[k] = test[result[k]].mean(axis = 1)
new_train['label'] = train['label']
new_test['label'] = test['label']
plt.figure(figsize=(30,30))
sns.heatmap(new_train.corr(), annot=True)
plt.savefig(path+'corr.jpg')
plt.show()
clf = CatBoostClassifier(verbose=0)
clf.fit(new_train.drop('label', axis =1), new_train['label'])
pred = clf.predict(new_test.drop('label', axis =1))
make_report(pred, new_test['label'])
Classification Report:
precision recall f1-score support
0.0 0.09 0.00 0.00 6879
1.0 0.21 0.00 0.01 27531
2.0 0.50 1.00 0.67 68954
3.0 0.21 0.00 0.01 27533
4.0 0.16 0.00 0.00 6882
accuracy 0.50 137779
macro avg 0.23 0.20 0.14 137779
weighted avg 0.35 0.50 0.34 137779
well f#@k
print(train[train.label == 0].shape)
print(train[train.label == 1].shape)
print(train[train.label == 2].shape)
print(train[train.label == 3].shape)
print(train[train.label == 4].shape)
(25016, 316) (100053, 316) (251677, 316) (100045, 316) (25017, 316)
clf = RandomForestClassifier()
clf.fit(train[feats], train['label'])
pred = clf.predict(test[feats])
make_report(pred, test['label'])
Classification Report:
precision recall f1-score support
0.0 0.23 0.00 0.01 6879
1.0 0.21 0.02 0.04 27531
2.0 0.51 0.97 0.67 68954
3.0 0.22 0.02 0.03 27533
4.0 0.22 0.00 0.01 6882
accuracy 0.50 137779
macro avg 0.28 0.20 0.15 137779
weighted avg 0.36 0.50 0.35 137779
train.label_2vsA.hist()
<AxesSubplot:>
clf = CatBoostClassifier(verbose=0)
clf.fit(train[feats], train['label_2vsA'])
pred = clf.predict(test[feats])
make_report(pred, test['label_2vsA'])
Classification Report:
E:\ProgramData\lib\site-packages\sklearn\metrics\_classification.py:1245: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) E:\ProgramData\lib\site-packages\sklearn\metrics\_classification.py:1245: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) E:\ProgramData\lib\site-packages\sklearn\metrics\_classification.py:1245: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
precision recall f1-score support
0 1.00 0.49 0.66 137779
1 0.00 0.00 0.00 0
accuracy 0.49 137779
macro avg 0.50 0.25 0.33 137779
weighted avg 1.00 0.49 0.66 137779
E:\ProgramData\lib\site-packages\mlxtend\plotting\plot_confusion_matrix.py:66: RuntimeWarning: invalid value encountered in true_divide
normed_conf_mat = conf_mat.astype('float') / total_samples
f = pd.DataFrame(clf.feature_importances_)
f['feature'] = feats
new_feat = f.sort_values(0)[-20:].feature.values
clf = CatBoostClassifier(verbose=0)
clf.fit(train[new_feat], train['label'])
pred = clf.predict(test[new_feat])
make_report(pred, test['label'])
Classification Report:
precision recall f1-score support
0.0 0.21 0.01 0.01 6879
1.0 0.22 0.00 0.01 27531
2.0 0.50 1.00 0.67 68954
3.0 0.22 0.00 0.01 27533
4.0 0.19 0.00 0.01 6882
accuracy 0.50 137779
macro avg 0.27 0.20 0.14 137779
weighted avg 0.36 0.50 0.34 137779
clf = XGBRegressor(max_depth =5, learning_rate =0.01, n_estimators=2000, colsample_bytree = 0.1 )
clf.fit(train[feats], train['target'])
pred = clf.predict(test[feats])
plt.hist(pred)
(array([9.0000e+00, 9.6000e+01, 8.2000e+02, 7.3960e+03, 3.8601e+04,
5.7046e+04, 2.7143e+04, 5.9160e+03, 7.2000e+02, 3.2000e+01]),
array([0.42878836, 0.44210118, 0.45541403, 0.46872684, 0.4820397 ,
0.4953525 , 0.5086653 , 0.52197814, 0.535291 , 0.54860383,
0.56191665], dtype=float32),
<BarContainer object of 10 artists>)
clf = LogisticRegression()
clf.fit(train[new_feat], train['label'])
pred = clf.predict(test[new_feat])
make_report(pred, test['label'])
E:\ProgramData\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
E:\ProgramData\lib\site-packages\sklearn\metrics\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
Classification Report:
E:\ProgramData\lib\site-packages\sklearn\metrics\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) E:\ProgramData\lib\site-packages\sklearn\metrics\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
precision recall f1-score support
0.0 0.00 0.00 0.00 6879
1.0 0.00 0.00 0.00 27531
2.0 0.50 1.00 0.67 68954
3.0 0.00 0.00 0.00 27533
4.0 0.00 0.00 0.00 6882
accuracy 0.50 137779
macro avg 0.10 0.20 0.13 137779
weighted avg 0.25 0.50 0.33 137779
clf = LogisticRegression()
clf.fit(train[feats], train['label_2vsA'])
pred = clf.predict(test[feats])
make_report(pred, test['label_2vsA'])
E:\ProgramData\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
Classification Report:
precision recall f1-score support
0 1.00 0.50 0.66 137779
1 0.00 0.00 0.00 0
accuracy 0.50 137779
macro avg 0.50 0.25 0.33 137779
weighted avg 1.00 0.50 0.66 137779
E:\ProgramData\lib\site-packages\sklearn\metrics\_classification.py:1245: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
E:\ProgramData\lib\site-packages\sklearn\metrics\_classification.py:1245: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
E:\ProgramData\lib\site-packages\sklearn\metrics\_classification.py:1245: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
E:\ProgramData\lib\site-packages\mlxtend\plotting\plot_confusion_matrix.py:66: RuntimeWarning: invalid value encountered in true_divide
normed_conf_mat = conf_mat.astype('float') / total_samples
for e in train.era.unique()[5:10]:
train[train.era == e].label.hist()
plt.show()
print(train[train.era == e].shape)
(2875, 316)
(3026, 316)
(3018, 316)
(3171, 316)
(3336, 316)
train[train.era == 'era1']
| id | era | data_type | feature_intelligence1 | feature_intelligence2 | feature_intelligence3 | feature_intelligence4 | feature_intelligence5 | feature_intelligence6 | feature_intelligence7 | feature_intelligence8 | feature_intelligence9 | feature_intelligence10 | feature_intelligence11 | feature_intelligence12 | feature_charisma1 | feature_charisma2 | feature_charisma3 | feature_charisma4 | feature_charisma5 | feature_charisma6 | feature_charisma7 | feature_charisma8 | feature_charisma9 | feature_charisma10 | ... | feature_wisdom25 | feature_wisdom26 | feature_wisdom27 | feature_wisdom28 | feature_wisdom29 | feature_wisdom30 | feature_wisdom31 | feature_wisdom32 | feature_wisdom33 | feature_wisdom34 | feature_wisdom35 | feature_wisdom36 | feature_wisdom37 | feature_wisdom38 | feature_wisdom39 | feature_wisdom40 | feature_wisdom41 | feature_wisdom42 | feature_wisdom43 | feature_wisdom44 | feature_wisdom45 | feature_wisdom46 | target | label | label_2vsA | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | n000315175b67977 | era1 | train | 0.00 | 0.50 | 0.25 | 0.00 | 0.50 | 0.25 | 0.25 | 0.25 | 0.75 | 0.75 | 0.25 | 0.25 | 1.00 | 0.75 | 0.50 | 1.00 | 0.50 | 0.00 | 0.50 | 0.50 | 0.00 | 0.00 | ... | 0.25 | 0.50 | 1.00 | 0.75 | 0.50 | 0.50 | 1.00 | 0.25 | 0.50 | 0.50 | 0.50 | 0.75 | 1.00 | 1.00 | 1.00 | 0.75 | 0.50 | 0.75 | 0.50 | 1.00 | 0.50 | 0.75 | 0.50 | 2.0 | 1 |
| 1 | n0014af834a96cdd | era1 | train | 0.00 | 0.00 | 0.00 | 0.25 | 0.50 | 0.00 | 0.00 | 0.25 | 0.50 | 0.50 | 0.00 | 0.50 | 0.00 | 0.50 | 0.50 | 0.50 | 0.50 | 0.50 | 0.25 | 0.25 | 0.50 | 0.00 | ... | 1.00 | 0.25 | 0.75 | 1.00 | 0.25 | 0.25 | 1.00 | 0.50 | 0.50 | 0.50 | 0.75 | 0.75 | 0.75 | 1.00 | 1.00 | 0.00 | 0.00 | 0.75 | 0.25 | 0.00 | 0.25 | 1.00 | 0.25 | 1.0 | 0 |
| 2 | n001c93979ac41d4 | era1 | train | 0.25 | 0.50 | 0.25 | 0.25 | 1.00 | 0.75 | 0.75 | 0.25 | 0.00 | 0.25 | 0.50 | 1.00 | 0.50 | 0.75 | 0.50 | 0.50 | 1.00 | 0.50 | 0.50 | 0.50 | 0.25 | 0.00 | ... | 0.00 | 0.50 | 0.75 | 0.00 | 1.00 | 0.50 | 0.50 | 0.75 | 1.00 | 0.75 | 1.00 | 0.25 | 0.50 | 0.25 | 0.50 | 0.00 | 0.00 | 0.50 | 1.00 | 0.00 | 0.25 | 0.75 | 0.25 | 1.0 | 0 |
| 3 | n0034e4143f22a13 | era1 | train | 1.00 | 0.00 | 0.00 | 0.50 | 0.50 | 0.25 | 0.25 | 0.75 | 0.25 | 0.50 | 0.50 | 0.50 | 0.75 | 0.50 | 1.00 | 0.50 | 0.50 | 0.00 | 1.00 | 0.00 | 0.75 | 0.00 | ... | 0.75 | 1.00 | 0.75 | 1.00 | 0.75 | 1.00 | 1.00 | 0.00 | 0.50 | 0.75 | 0.75 | 1.00 | 0.75 | 1.00 | 1.00 | 0.75 | 0.75 | 1.00 | 1.00 | 0.75 | 1.00 | 1.00 | 0.25 | 1.0 | 0 |
| 4 | n00679d1a636062f | era1 | train | 0.25 | 0.25 | 0.25 | 0.25 | 0.00 | 0.25 | 0.50 | 0.25 | 0.25 | 0.50 | 0.25 | 0.25 | 0.75 | 0.50 | 0.00 | 0.50 | 0.50 | 0.25 | 0.00 | 0.50 | 0.00 | 0.50 | ... | 0.50 | 0.75 | 0.75 | 0.50 | 0.75 | 0.25 | 0.75 | 0.50 | 0.50 | 0.25 | 0.25 | 0.75 | 0.50 | 0.75 | 0.75 | 0.25 | 0.50 | 0.75 | 0.00 | 0.50 | 0.25 | 0.75 | 0.75 | 3.0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2403 | nff8c4db7e8c26ab | era1 | train | 0.75 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | 0.75 | 1.00 | 0.00 | 0.00 | 0.75 | 1.00 | 0.00 | 0.50 | 0.00 | 0.00 | 0.00 | 0.00 | 0.50 | 0.50 | 0.25 | 0.25 | ... | 0.00 | 1.00 | 0.00 | 0.75 | 0.50 | 0.25 | 0.00 | 0.00 | 1.00 | 0.00 | 0.25 | 0.25 | 0.25 | 0.00 | 0.00 | 0.25 | 0.50 | 0.00 | 1.00 | 0.25 | 0.25 | 0.00 | 0.50 | 2.0 | 1 |
| 2404 | nffaef40da241883 | era1 | train | 0.50 | 1.00 | 0.75 | 0.25 | 1.00 | 1.00 | 1.00 | 0.00 | 0.75 | 1.00 | 0.50 | 1.00 | 0.50 | 0.50 | 0.75 | 0.50 | 0.50 | 0.75 | 0.50 | 0.50 | 1.00 | 0.25 | ... | 0.75 | 0.75 | 1.00 | 1.00 | 0.75 | 1.00 | 1.00 | 0.50 | 0.50 | 0.75 | 0.75 | 0.50 | 0.75 | 1.00 | 1.00 | 0.00 | 0.00 | 0.50 | 0.25 | 0.00 | 1.00 | 0.75 | 0.50 | 2.0 | 1 |
| 2405 | nffd795bbc001447 | era1 | train | 0.75 | 0.50 | 0.50 | 0.50 | 0.50 | 0.25 | 0.25 | 0.25 | 0.75 | 0.75 | 0.75 | 0.25 | 0.00 | 0.00 | 1.00 | 0.50 | 0.50 | 0.00 | 0.25 | 0.75 | 1.00 | 0.25 | ... | 0.75 | 0.25 | 0.25 | 0.50 | 0.75 | 0.00 | 1.00 | 0.50 | 0.50 | 1.00 | 1.00 | 0.75 | 0.75 | 0.75 | 1.00 | 0.00 | 0.00 | 0.75 | 1.00 | 0.00 | 0.25 | 0.50 | 0.50 | 2.0 | 1 |
| 2406 | nfff8f6044464a9e | era1 | train | 0.75 | 0.50 | 0.50 | 0.75 | 0.75 | 0.25 | 0.50 | 0.50 | 0.50 | 0.50 | 0.50 | 0.75 | 0.75 | 0.00 | 1.00 | 0.75 | 0.50 | 0.75 | 0.50 | 0.75 | 0.00 | 1.00 | ... | 1.00 | 0.75 | 1.00 | 1.00 | 0.75 | 0.75 | 0.75 | 0.50 | 0.75 | 0.00 | 0.00 | 0.50 | 0.50 | 0.75 | 0.75 | 1.00 | 1.00 | 0.50 | 0.50 | 1.00 | 0.75 | 0.75 | 0.50 | 2.0 | 1 |
| 2407 | nffff520391f69ad | era1 | train | 0.50 | 0.50 | 0.50 | 0.50 | 0.50 | 0.00 | 0.25 | 0.50 | 0.50 | 0.50 | 0.75 | 0.50 | 0.75 | 0.00 | 0.50 | 0.25 | 0.25 | 0.00 | 0.50 | 0.00 | 0.50 | 0.75 | ... | 0.25 | 0.25 | 0.50 | 0.50 | 0.25 | 0.50 | 0.00 | 0.25 | 0.25 | 0.00 | 0.00 | 0.00 | 0.25 | 0.00 | 0.00 | 0.50 | 0.50 | 0.00 | 0.25 | 0.25 | 0.50 | 0.50 | 0.25 | 1.0 | 0 |
2408 rows × 316 columns
test.era.unique()
array(['era121', 'era122', 'era123', 'era124', 'era125', 'era126',
'era127', 'era128', 'era129', 'era130', 'era131', 'era132',
'era197', 'era198', 'era199', 'era200', 'era201', 'era202',
'era203', 'era204', 'era205', 'era206', 'era207', 'era208',
'era209', 'era210', 'era211', 'era212'], dtype=object)
clf = DecisionTreeRegressor()
clf.fit(train[train.era == 'era1'][feats], train[train.era == 'era1']['target'])
pred = clf.predict(train[train.era == 'era2'][feats])
make_report(pred*4, train[train.era == 'era2']['label'])
feats = train.columns[3:-3]
train[(train.label==0) | (train.label==4)]
| id | era | data_type | feature_intelligence1 | feature_intelligence2 | feature_intelligence3 | feature_intelligence4 | feature_intelligence5 | feature_intelligence6 | feature_intelligence7 | feature_intelligence8 | feature_intelligence9 | feature_intelligence10 | feature_intelligence11 | feature_intelligence12 | feature_charisma1 | feature_charisma2 | feature_charisma3 | feature_charisma4 | feature_charisma5 | feature_charisma6 | feature_charisma7 | feature_charisma8 | feature_charisma9 | feature_charisma10 | ... | feature_wisdom25 | feature_wisdom26 | feature_wisdom27 | feature_wisdom28 | feature_wisdom29 | feature_wisdom30 | feature_wisdom31 | feature_wisdom32 | feature_wisdom33 | feature_wisdom34 | feature_wisdom35 | feature_wisdom36 | feature_wisdom37 | feature_wisdom38 | feature_wisdom39 | feature_wisdom40 | feature_wisdom41 | feature_wisdom42 | feature_wisdom43 | feature_wisdom44 | feature_wisdom45 | feature_wisdom46 | target | label | label_2vsA | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 12 | n00d8f184e5b24e8 | era1 | train | 1.00 | 0.00 | 0.00 | 1.00 | 0.25 | 0.50 | 0.75 | 0.75 | 0.50 | 0.50 | 1.00 | 0.25 | 0.00 | 0.50 | 0.25 | 1.00 | 0.50 | 0.00 | 0.00 | 0.75 | 1.00 | 0.25 | ... | 0.50 | 0.25 | 0.25 | 0.75 | 0.25 | 1.00 | 1.00 | 0.00 | 0.25 | 0.50 | 0.25 | 0.75 | 0.75 | 0.75 | 0.75 | 0.25 | 0.50 | 1.00 | 0.25 | 0.25 | 1.00 | 1.00 | 0.0 | 0.0 | 0 |
| 14 | n011ecdf8e5491eb | era1 | train | 0.50 | 0.25 | 0.25 | 0.50 | 0.75 | 0.50 | 0.50 | 0.50 | 0.50 | 0.50 | 0.25 | 0.75 | 0.25 | 0.50 | 0.25 | 1.00 | 0.50 | 0.25 | 0.00 | 0.25 | 0.75 | 0.25 | ... | 0.50 | 0.25 | 0.25 | 0.25 | 0.25 | 0.75 | 0.50 | 0.00 | 0.25 | 0.50 | 0.50 | 0.50 | 0.25 | 0.25 | 0.25 | 0.25 | 0.25 | 0.50 | 0.25 | 0.25 | 0.75 | 0.50 | 1.0 | 4.0 | 0 |
| 33 | n02ad35bb1903b21 | era1 | train | 0.75 | 0.50 | 0.50 | 0.75 | 0.00 | 1.00 | 1.00 | 0.50 | 0.25 | 0.25 | 0.75 | 0.00 | 0.75 | 1.00 | 0.25 | 0.50 | 0.75 | 0.50 | 0.00 | 1.00 | 0.75 | 0.00 | ... | 0.00 | 1.00 | 1.00 | 1.00 | 0.00 | 0.25 | 1.00 | 1.00 | 0.75 | 0.25 | 0.00 | 1.00 | 0.75 | 1.00 | 1.00 | 0.25 | 0.00 | 1.00 | 0.75 | 0.25 | 0.50 | 0.25 | 0.0 | 0.0 | 0 |
| 34 | n02c9146bba8182c | era1 | train | 0.75 | 0.00 | 0.00 | 0.75 | 0.25 | 0.50 | 0.50 | 0.75 | 0.00 | 0.00 | 0.75 | 0.00 | 0.25 | 0.50 | 0.25 | 0.75 | 0.50 | 0.25 | 0.00 | 0.25 | 1.00 | 0.75 | ... | 0.50 | 0.25 | 1.00 | 0.75 | 0.00 | 1.00 | 1.00 | 0.25 | 0.75 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.75 | 1.00 | 1.00 | 0.50 | 0.75 | 1.00 | 1.00 | 1.0 | 4.0 | 0 |
| 44 | n03a4981030f4016 | era1 | train | 0.75 | 0.50 | 0.50 | 0.75 | 1.00 | 0.75 | 0.50 | 0.75 | 0.50 | 0.50 | 0.50 | 1.00 | 0.75 | 0.00 | 0.75 | 0.50 | 0.50 | 0.00 | 0.50 | 0.75 | 0.50 | 0.50 | ... | 0.75 | 0.50 | 1.00 | 0.75 | 0.25 | 0.25 | 0.75 | 0.50 | 0.50 | 0.25 | 0.00 | 1.00 | 0.75 | 1.00 | 0.75 | 1.00 | 1.00 | 0.75 | 1.00 | 1.00 | 0.50 | 0.75 | 0.0 | 0.0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 501762 | nfdc86c866cd6a7a | era120 | train | 1.00 | 0.00 | 0.00 | 1.00 | 0.50 | 0.75 | 0.75 | 1.00 | 0.50 | 0.75 | 1.00 | 0.50 | 0.25 | 0.00 | 0.25 | 0.25 | 0.25 | 0.00 | 0.00 | 0.00 | 0.75 | 0.50 | ... | 0.00 | 0.00 | 0.25 | 0.00 | 0.75 | 0.50 | 0.00 | 0.50 | 0.00 | 0.25 | 0.00 | 0.50 | 0.00 | 0.25 | 0.25 | 0.50 | 0.25 | 0.00 | 0.00 | 0.50 | 0.75 | 0.25 | 0.0 | 0.0 | 0 |
| 501763 | nfdca18be7894d4d | era120 | train | 1.00 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.00 | 1.00 | 0.50 | 1.00 | 0.00 | 0.75 | 1.00 | 0.00 | 0.00 | ... | 0.00 | 0.50 | 0.00 | 0.00 | 0.00 | 0.00 | 0.25 | 1.00 | 0.50 | 0.25 | 0.25 | 1.00 | 0.25 | 0.25 | 0.25 | 0.00 | 0.00 | 1.00 | 0.25 | 0.00 | 0.00 | 0.00 | 1.0 | 4.0 | 0 |
| 501771 | nfe01f24f7210e81 | era120 | train | 1.00 | 0.00 | 0.00 | 1.00 | 0.50 | 0.75 | 0.75 | 1.00 | 0.50 | 0.50 | 1.00 | 0.50 | 0.50 | 0.75 | 0.75 | 0.50 | 0.50 | 0.00 | 0.75 | 0.50 | 0.00 | 0.75 | ... | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.50 | 0.50 | 0.50 | 0.25 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.75 | 1.00 | 1.00 | 0.75 | 1.0 | 4.0 | 0 |
| 501790 | nfee4380e815f087 | era120 | train | 0.25 | 1.00 | 1.00 | 0.25 | 0.50 | 0.75 | 0.75 | 0.50 | 1.00 | 1.00 | 0.50 | 0.50 | 0.25 | 0.50 | 0.50 | 1.00 | 0.50 | 1.00 | 0.75 | 0.50 | 0.75 | 0.75 | ... | 0.50 | 0.25 | 0.25 | 0.25 | 1.00 | 0.00 | 0.25 | 0.50 | 1.00 | 1.00 | 1.00 | 0.00 | 0.25 | 0.00 | 0.50 | 0.50 | 0.50 | 0.50 | 0.00 | 1.00 | 0.00 | 0.25 | 1.0 | 4.0 | 0 |
| 501795 | nff05523e8f6f181 | era120 | train | 1.00 | 0.00 | 0.00 | 1.00 | 0.00 | 1.00 | 1.00 | 1.00 | 0.00 | 0.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.25 | 0.00 | 0.25 | 0.00 | 0.50 | 1.00 | 0.75 | ... | 0.75 | 1.00 | 0.00 | 0.25 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | 0.25 | 0.25 | 1.00 | 0.00 | 0.00 | 0.00 | 0.25 | 1.00 | 1.00 | 1.00 | 0.25 | 0.00 | 0.00 | 1.0 | 4.0 | 0 |
50033 rows × 316 columns
clf = DecisionTreeClassifier()
clf.fit(train[(train.label==0) | (train.label==4)][feats], train[(train.label==0) | (train.label==4)]['label'])
pred = clf.predict(test[(test.label==0) | (test.label==4)][feats])
make_report(pred, test[(test.label==0) | (test.label==4)]['label'])
Classification Report:
precision recall f1-score support
0.0 0.50 0.50 0.50 6879
4.0 0.50 0.51 0.51 6882
accuracy 0.50 13761
macro avg 0.50 0.50 0.50 13761
weighted avg 0.50 0.50 0.50 13761
clf = DecisionTreeClassifier()
clf.fit(train[(train.label==1) | (train.label==3)][feats], train[(train.label==1) | (train.label==3)]['label'])
pred = clf.predict(test[(test.label==1) | (test.label==3)][feats])
make_report(pred, test[(test.label==1) | (test.label==3)]['label'])
Classification Report:
precision recall f1-score support
1.0 0.50 0.49 0.50 27531
3.0 0.50 0.50 0.50 27533
accuracy 0.50 55064
macro avg 0.50 0.50 0.50 55064
weighted avg 0.50 0.50 0.50 55064
clf = DecisionTreeClassifier()
clf.fit(train[train.label!=2][feats], train[train.label!=2]['label'])
pred = clf.predict(test[test.label!=2][feats])
make_report(pred, test[test.label!=2]['label'])
Classification Report:
precision recall f1-score support
0.0 0.13 0.15 0.14 6879
1.0 0.41 0.40 0.40 27531
3.0 0.41 0.39 0.40 27533
4.0 0.13 0.15 0.14 6882
accuracy 0.35 68825
macro avg 0.27 0.27 0.27 68825
weighted avg 0.36 0.35 0.35 68825
plt.hist(clf.predict(train[train.label==2][feats]))
(array([ 21453., 0., 104925., 0., 0., 0., 0.,
104424., 0., 20875.]),
array([0. , 0.4, 0.8, 1.2, 1.6, 2. , 2.4, 2.8, 3.2, 3.6, 4. ]),
<BarContainer object of 10 artists>)
a = 0
b = 1
clf = DecisionTreeClassifier()
clf.fit(train[(train.label==a) | (train.label==b)][feats], train[(train.label==a) | (train.label==b)]['label'])
pred = clf.predict(test[(test.label==a) | (test.label==b)][feats])
make_report(pred, test[(test.label==a) | (test.label==b)]['label'])
Classification Report:
precision recall f1-score support
0.0 0.26 0.31 0.28 6879
1.0 0.82 0.77 0.80 27531
accuracy 0.68 34410
macro avg 0.54 0.54 0.54 34410
weighted avg 0.71 0.68 0.69 34410
a = 1
b = 2
clf = DecisionTreeClassifier()
clf.fit(train[(train.label==a) | (train.label==b)][feats], train[(train.label==a) | (train.label==b)]['label'])
pred = clf.predict(test[(test.label==a) | (test.label==b)][feats])
make_report(pred, test[(test.label==a) | (test.label==b)]['label'])
Classification Report:
precision recall f1-score support
1.0 0.30 0.34 0.32 27531
2.0 0.72 0.68 0.70 68954
accuracy 0.58 96485
macro avg 0.51 0.51 0.51 96485
weighted avg 0.60 0.58 0.59 96485
a = 2
b = 3
clf = DecisionTreeClassifier()
clf.fit(train[(train.label==a) | (train.label==b)][feats], train[(train.label==a) | (train.label==b)]['label'])
pred = clf.predict(test[(test.label==a) | (test.label==b)][feats])
make_report(pred, test[(test.label==a) | (test.label==b)]['label'])
Classification Report:
precision recall f1-score support
2.0 0.72 0.68 0.70 68954
3.0 0.30 0.34 0.32 27533
accuracy 0.59 96487
macro avg 0.51 0.51 0.51 96487
weighted avg 0.60 0.59 0.59 96487
a = 3
b = 4
clf = DecisionTreeClassifier()
clf.fit(train[(train.label==a) | (train.label==b)][feats], train[(train.label==a) | (train.label==b)]['label'])
pred = clf.predict(test[(test.label==a) | (test.label==b)][feats])
make_report(pred, test[(test.label==a) | (test.label==b)]['label'])
Classification Report:
precision recall f1-score support
3.0 0.82 0.78 0.80 27533
4.0 0.26 0.31 0.29 6882
accuracy 0.69 34415
macro avg 0.54 0.55 0.54 34415
weighted avg 0.71 0.69 0.70 34415
train
| id | era | data_type | feature_intelligence1 | feature_intelligence2 | feature_intelligence3 | feature_intelligence4 | feature_intelligence5 | feature_intelligence6 | feature_intelligence7 | feature_intelligence8 | feature_intelligence9 | feature_intelligence10 | feature_intelligence11 | feature_intelligence12 | feature_charisma1 | feature_charisma2 | feature_charisma3 | feature_charisma4 | feature_charisma5 | feature_charisma6 | feature_charisma7 | feature_charisma8 | feature_charisma9 | feature_charisma10 | ... | feature_wisdom25 | feature_wisdom26 | feature_wisdom27 | feature_wisdom28 | feature_wisdom29 | feature_wisdom30 | feature_wisdom31 | feature_wisdom32 | feature_wisdom33 | feature_wisdom34 | feature_wisdom35 | feature_wisdom36 | feature_wisdom37 | feature_wisdom38 | feature_wisdom39 | feature_wisdom40 | feature_wisdom41 | feature_wisdom42 | feature_wisdom43 | feature_wisdom44 | feature_wisdom45 | feature_wisdom46 | target | label | label_2vsA | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | n000315175b67977 | era1 | train | 0.00 | 0.50 | 0.25 | 0.00 | 0.50 | 0.25 | 0.25 | 0.25 | 0.75 | 0.75 | 0.25 | 0.25 | 1.00 | 0.75 | 0.50 | 1.00 | 0.50 | 0.00 | 0.50 | 0.50 | 0.00 | 0.00 | ... | 0.25 | 0.50 | 1.00 | 0.75 | 0.50 | 0.50 | 1.00 | 0.25 | 0.50 | 0.50 | 0.50 | 0.75 | 1.00 | 1.00 | 1.00 | 0.75 | 0.50 | 0.75 | 0.50 | 1.00 | 0.50 | 0.75 | 0.50 | 2.0 | 1 |
| 1 | n0014af834a96cdd | era1 | train | 0.00 | 0.00 | 0.00 | 0.25 | 0.50 | 0.00 | 0.00 | 0.25 | 0.50 | 0.50 | 0.00 | 0.50 | 0.00 | 0.50 | 0.50 | 0.50 | 0.50 | 0.50 | 0.25 | 0.25 | 0.50 | 0.00 | ... | 1.00 | 0.25 | 0.75 | 1.00 | 0.25 | 0.25 | 1.00 | 0.50 | 0.50 | 0.50 | 0.75 | 0.75 | 0.75 | 1.00 | 1.00 | 0.00 | 0.00 | 0.75 | 0.25 | 0.00 | 0.25 | 1.00 | 0.25 | 1.0 | 0 |
| 2 | n001c93979ac41d4 | era1 | train | 0.25 | 0.50 | 0.25 | 0.25 | 1.00 | 0.75 | 0.75 | 0.25 | 0.00 | 0.25 | 0.50 | 1.00 | 0.50 | 0.75 | 0.50 | 0.50 | 1.00 | 0.50 | 0.50 | 0.50 | 0.25 | 0.00 | ... | 0.00 | 0.50 | 0.75 | 0.00 | 1.00 | 0.50 | 0.50 | 0.75 | 1.00 | 0.75 | 1.00 | 0.25 | 0.50 | 0.25 | 0.50 | 0.00 | 0.00 | 0.50 | 1.00 | 0.00 | 0.25 | 0.75 | 0.25 | 1.0 | 0 |
| 3 | n0034e4143f22a13 | era1 | train | 1.00 | 0.00 | 0.00 | 0.50 | 0.50 | 0.25 | 0.25 | 0.75 | 0.25 | 0.50 | 0.50 | 0.50 | 0.75 | 0.50 | 1.00 | 0.50 | 0.50 | 0.00 | 1.00 | 0.00 | 0.75 | 0.00 | ... | 0.75 | 1.00 | 0.75 | 1.00 | 0.75 | 1.00 | 1.00 | 0.00 | 0.50 | 0.75 | 0.75 | 1.00 | 0.75 | 1.00 | 1.00 | 0.75 | 0.75 | 1.00 | 1.00 | 0.75 | 1.00 | 1.00 | 0.25 | 1.0 | 0 |
| 4 | n00679d1a636062f | era1 | train | 0.25 | 0.25 | 0.25 | 0.25 | 0.00 | 0.25 | 0.50 | 0.25 | 0.25 | 0.50 | 0.25 | 0.25 | 0.75 | 0.50 | 0.00 | 0.50 | 0.50 | 0.25 | 0.00 | 0.50 | 0.00 | 0.50 | ... | 0.50 | 0.75 | 0.75 | 0.50 | 0.75 | 0.25 | 0.75 | 0.50 | 0.50 | 0.25 | 0.25 | 0.75 | 0.50 | 0.75 | 0.75 | 0.25 | 0.50 | 0.75 | 0.00 | 0.50 | 0.25 | 0.75 | 0.75 | 3.0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 501803 | nff6a8a8feaeeb52 | era120 | train | 0.50 | 0.50 | 0.25 | 0.00 | 0.00 | 0.50 | 0.75 | 0.00 | 0.75 | 0.75 | 0.25 | 0.00 | 1.00 | 0.00 | 0.75 | 0.00 | 0.50 | 1.00 | 0.25 | 0.25 | 0.00 | 0.50 | ... | 0.25 | 0.75 | 0.25 | 0.00 | 0.00 | 0.25 | 0.50 | 0.00 | 0.75 | 0.50 | 0.50 | 0.75 | 0.75 | 0.50 | 0.50 | 0.75 | 0.50 | 0.50 | 0.75 | 0.25 | 0.25 | 0.25 | 0.50 | 2.0 | 1 |
| 501804 | nff6af62a0996372 | era120 | train | 1.00 | 0.00 | 0.00 | 1.00 | 0.50 | 0.75 | 0.75 | 1.00 | 0.00 | 0.00 | 1.00 | 0.50 | 0.25 | 0.75 | 0.75 | 0.50 | 0.50 | 1.00 | 1.00 | 0.00 | 0.25 | 1.00 | ... | 0.75 | 0.50 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.50 | 0.00 | 0.75 | 0.75 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.00 | 0.75 | 1.00 | 1.00 | 0.75 | 3.0 | 0 |
| 501805 | nff9288983b8c040 | era120 | train | 0.75 | 0.50 | 0.50 | 0.50 | 0.25 | 0.50 | 0.25 | 0.50 | 0.25 | 0.25 | 0.75 | 0.25 | 1.00 | 0.25 | 0.25 | 0.75 | 0.75 | 1.00 | 0.00 | 0.00 | 0.75 | 1.00 | ... | 1.00 | 1.00 | 0.75 | 0.75 | 1.00 | 0.00 | 0.75 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.75 | 0.25 | 1.00 | 1.00 | 1.00 | 0.25 | 0.00 | 0.00 | 0.25 | 1.0 | 0 |
| 501806 | nffaab4e1cacc4b1 | era120 | train | 0.25 | 0.25 | 0.25 | 0.50 | 0.00 | 1.00 | 1.00 | 0.50 | 0.25 | 0.25 | 0.25 | 0.00 | 1.00 | 1.00 | 0.00 | 0.50 | 0.75 | 0.25 | 1.00 | 0.25 | 0.00 | 0.25 | ... | 0.25 | 0.50 | 0.75 | 0.25 | 0.25 | 0.50 | 0.75 | 0.50 | 0.50 | 0.50 | 0.50 | 0.50 | 0.50 | 0.75 | 0.75 | 0.75 | 0.75 | 0.75 | 0.50 | 0.50 | 0.25 | 0.75 | 0.50 | 2.0 | 1 |
| 501807 | nffba5460b572cfa | era120 | train | 0.75 | 0.50 | 0.50 | 0.75 | 0.75 | 0.00 | 0.00 | 0.75 | 0.00 | 0.00 | 0.50 | 0.75 | 0.75 | 0.75 | 0.50 | 0.75 | 0.00 | 0.00 | 0.50 | 0.75 | 0.50 | 0.25 | ... | 0.75 | 1.00 | 0.50 | 0.75 | 0.50 | 0.75 | 0.50 | 0.50 | 1.00 | 0.50 | 0.50 | 0.75 | 0.50 | 0.50 | 0.50 | 0.25 | 0.50 | 0.75 | 1.00 | 0.25 | 0.75 | 0.50 | 0.50 | 2.0 | 1 |
501808 rows × 316 columns
train.describe()
| feature_intelligence1 | feature_intelligence2 | feature_intelligence3 | feature_intelligence4 | feature_intelligence5 | feature_intelligence6 | feature_intelligence7 | feature_intelligence8 | feature_intelligence9 | feature_intelligence10 | feature_intelligence11 | feature_intelligence12 | feature_charisma1 | feature_charisma2 | feature_charisma3 | feature_charisma4 | feature_charisma5 | feature_charisma6 | feature_charisma7 | feature_charisma8 | feature_charisma9 | feature_charisma10 | feature_charisma11 | feature_charisma12 | feature_charisma13 | ... | feature_wisdom25 | feature_wisdom26 | feature_wisdom27 | feature_wisdom28 | feature_wisdom29 | feature_wisdom30 | feature_wisdom31 | feature_wisdom32 | feature_wisdom33 | feature_wisdom34 | feature_wisdom35 | feature_wisdom36 | feature_wisdom37 | feature_wisdom38 | feature_wisdom39 | feature_wisdom40 | feature_wisdom41 | feature_wisdom42 | feature_wisdom43 | feature_wisdom44 | feature_wisdom45 | feature_wisdom46 | target | label | label_2vsA | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | ... | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 | 501808.000000 |
| mean | 0.499981 | 0.499979 | 0.499979 | 0.499981 | 0.499977 | 0.499977 | 0.499977 | 0.499981 | 0.499980 | 0.499980 | 0.499981 | 0.499977 | 0.499968 | 0.499976 | 0.499974 | 0.499977 | 0.499976 | 0.499971 | 0.499974 | 0.499974 | 0.499986 | 0.499977 | 0.499979 | 0.499980 | 0.499977 | ... | 0.499969 | 0.499982 | 0.499979 | 0.499974 | 0.499981 | 0.499979 | 0.499979 | 0.499976 | 0.499974 | 0.499978 | 0.499981 | 0.499982 | 0.499979 | 0.499982 | 0.499982 | 0.499974 | 0.499980 | 0.499982 | 0.499980 | 0.499974 | 0.499979 | 0.499971 | 0.499997 | 1.999988 | 0.501540 |
| std | 0.353596 | 0.353593 | 0.353593 | 0.353596 | 0.353587 | 0.353587 | 0.353587 | 0.353596 | 0.352099 | 0.352099 | 0.353596 | 0.353587 | 0.352986 | 0.334986 | 0.337437 | 0.338557 | 0.313514 | 0.353014 | 0.320992 | 0.291361 | 0.353473 | 0.348416 | 0.343023 | 0.343407 | 0.340066 | ... | 0.351413 | 0.350824 | 0.353143 | 0.348778 | 0.353596 | 0.347689 | 0.352772 | 0.302390 | 0.341651 | 0.344672 | 0.353596 | 0.352151 | 0.352772 | 0.353139 | 0.353139 | 0.351328 | 0.350662 | 0.352151 | 0.352965 | 0.351328 | 0.347689 | 0.353419 | 0.223268 | 0.893072 | 0.499998 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | ... | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.250000 | 0.500000 | 2.000000 | 0.000000 |
| 50% | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | ... | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 0.500000 | 2.000000 | 1.000000 |
| 75% | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | ... | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.750000 | 0.500000 | 2.000000 | 1.000000 |
| max | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | ... | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 4.000000 | 1.000000 |
8 rows × 313 columns
a = train[train.label == 0]
a = a.append(train[train.label == 1].sample(15000, random_state=42))
a = a.append(train[train.label == 2].sample(10000, random_state=42))
a = a.append(train[train.label == 3].sample(15000, random_state=42))
a = a.append(train[train.label == 4])
clf = DecisionTreeClassifier()
clf.fit(a[feats], a['label'])
pred = clf.predict(test[feats])
make_report(pred, test['label'])
Classification Report:
precision recall f1-score support
0.0 0.06 0.30 0.11 6879
1.0 0.20 0.19 0.19 27531
2.0 0.57 0.17 0.26 68954
3.0 0.19 0.19 0.19 27533
4.0 0.07 0.31 0.11 6882
accuracy 0.19 137779
macro avg 0.22 0.23 0.17 137779
weighted avg 0.37 0.19 0.22 137779
import pandas as pd
import os
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
import seaborn as sns
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
class MyDataset(torch.utils.data.Dataset):
def __init__(self, x, y):
self.x = torch.tensor(x.values).float()
self.y = torch.tensor(y.values).long()
def __len__(self):
return len(self.x)
def __getitem__(self, idx):
x = self.x[idx]
y = self.y[idx]
return(x, y)
BATCH_SIZE = 256
train_dataset = MyDataset(train[feats], train['label'])
dataLoader_train = torch.utils.data.DataLoader(train_dataset,
batch_size=BATCH_SIZE,
shuffle=True)
test_dataset = MyDataset(test[feats], test['label'])
dataLoader_test = torch.utils.data.DataLoader(test_dataset,
batch_size=BATCH_SIZE,
shuffle=True)
class Model(torch.nn.Module):
def __init__(self, input_shape):
super().__init__()
self.conv = torch.nn.Sequential(
torch.nn.Conv1d(1, 256, 5),
torch.nn.ReLU(),
# torch.nn.Conv1d(1, 256,5),
# torch.nn.ReLU(),
)
self.flatten = torch.nn.Sequential(torch.nn.AdaptiveMaxPool1d(1), torch.nn.Flatten())
self.fc = torch.nn.Sequential(
torch.nn.ReLU(),
torch.nn.Linear(256, 5),
)
def forward(self, x):
x = self.conv(x)
x = self.flatten(x)
x = self.fc(x)
return x
model = Model(len(feats))
print(model)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=5e-3)
Model(
(conv): Sequential(
(0): Conv1d(1, 256, kernel_size=(5,), stride=(1,))
(1): ReLU()
)
(flatten): Sequential(
(0): AdaptiveMaxPool1d(output_size=1)
(1): Flatten(start_dim=1, end_dim=-1)
)
(fc): Sequential(
(0): ReLU()
(1): Linear(in_features=256, out_features=5, bias=True)
)
)
n_epochs = 10
print('started!')
for epoch in range(n_epochs):
train_batch_loss = 0
model.train()
for step, batch in enumerate(dataLoader_train):
x = batch[0]
y = batch[1]
x = x.unsqueeze(1)
optimizer.zero_grad()
outputs = model(x = x)
loss = criterion(outputs, y)
loss.backward()
optimizer.step()
train_batch_loss += loss.item()
optimizer.step()
optimizer.zero_grad()
test_batch_loss = 0
model.eval()
with torch.no_grad():
for step, batch in enumerate(dataLoader_test):
x = batch[0]
y = batch[1]
x = x.unsqueeze(1)
outputs = model(x = x)
loss = criterion(outputs, y)
test_batch_loss += loss.item()
print('epoch {}/{} finished with train loss: {} and test loss: {}'.format(epoch+1, n_epochs,
train_batch_loss / len(dataLoader_train),
test_batch_loss / len(dataLoader_test)))
torch.save(model.state_dict(), './model_two_input')
started! epoch 1/10 finished with train loss: 1.2806938938313999 and test loss: 1.2683333481397612 epoch 2/10 finished with train loss: 1.2751716581429224 and test loss: 1.271874274305156 epoch 3/10 finished with train loss: 1.2745362805931133 and test loss: 1.2693557801184716 epoch 4/10 finished with train loss: 1.2742652409177602 and test loss: 1.267664607690305 epoch 5/10 finished with train loss: 1.2738433708529397 and test loss: 1.2685914004225014 epoch 6/10 finished with train loss: 1.2739113095705148 and test loss: 1.2685656728903747 epoch 7/10 finished with train loss: 1.2738674873602993 and test loss: 1.267589208809918 epoch 8/10 finished with train loss: 1.273878046470537 and test loss: 1.267319800902386 epoch 9/10 finished with train loss: 1.27393921688465 and test loss: 1.267364300690688 epoch 10/10 finished with train loss: 1.2737680308251038 and test loss: 1.2687691717731708